Preprints
https://doi.org/10.5194/essd-2021-217
https://doi.org/10.5194/essd-2021-217

  09 Jul 2021

09 Jul 2021

Review status: this preprint is currently under review for the journal ESSD.

Development of East Asia Regional Reanalysis based on advanced hybrid gain data assimilation method and evaluation with E3DVAR, ERA-5, and ERA-Interim reanalysis

Eun-Gyeong Yang, Hyun Mee Kim, and Dae-Hui Kim Eun-Gyeong Yang et al.
  • Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric Science, Yonsei University, Seoul, Republic of Korea

Abstract. The East Asia Regional Reanalysis (EARR) system is developed based on the advanced hybrid gain data assimilation method (AdvHG) using Weather Research and Forecasting (WRF) model and conventional observations. Based on EARR, the high-resolution regional reanalysis and reforecast fields are produced with 12 km horizontal resolution over East Asia for 2010–2019. The newly proposed AdvHG is based on the hybrid gain approach, weighting two different analysis for an optimal analysis. The AdvHG is different from the hybrid gain in that 1) E3DVAR is used instead of EnKF, 2) 6 h forecast of ERA5 is used to be more consistent with WRF, and 3) the pre-existing, state-of-the-art reanalysis is used. Thus, the AdvHG can be regarded as an efficient approach to generate regional reanalysis dataset due to cost savings as well as the use of the state-of-the-art reanalysis. The upper air variables of EARR are verified with those of ERA5 for January and July 2017 and the two-year period of 2017–2018. For upper air variables, ERA5 outperforms EARR over two years, whereas EARR outperforms (shows comparable performance to) ERA-I and E3DVAR for January in 2017 (July in 2017). EARR better represents precipitation than ERA5 for January and July in 2017. Therefore, though the uncertainties of upper air variables of EARR need to be considered when analyzing them, the precipitation of EARR is more accurate than that of ERA5 for both two seasons. The EARR data presented here can be downloaded from https://doi.org/10.7910/DVN/7P8MZT for data on pressure levels and https://doi.org/10.7910/DVN/Q07VRC for precipitation.

Eun-Gyeong Yang et al.

Status: open (until 06 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-217', Anonymous Referee #1, 04 Aug 2021 reply
  • RC2: 'Comment on essd-2021-217', Anonymous Referee #2, 12 Oct 2021 reply

Eun-Gyeong Yang et al.

Data sets

East Asia Regional Reanalysis 6 hourly data on pressure levels from 2010 to 2019 Eun-Gyeong Yang, Hyun Mee Kim https://doi.org/10.7910/DVN/7P8MZT

East Asia Regional Reanalysis 6 hourly precipitation data from 2010 to 2019 Eun-Gyeong Yang, Hyun Mee Kim https://doi.org/10.7910/DVN/Q07VRC

Eun-Gyeong Yang et al.

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Short summary
The East Asia Regional Reanalysis (EARR) system is developed based on the advanced hybrid gain data assimilation method (AdvHG) using Weather Research and Forecasting (WRF) model and conventional observations. Based on EARR, the high-resolution regional reanalysis and reforecast fields are produced with 12 km horizontal resolution over East Asia for 2010–2019. Compared to ERA5, EARR better represents precipitation for January and July in 2017 over East Asia.